Remote Python/Typescript Engineer

Turing

2d•Remote

About The Position

Turing is seeking a high-taste engineer to support work on preference data and rubric creation for AI projects. The role involves designing and refining evaluation criteria, reviewing data carefully, and developing the pipelines and infrastructure used for data generation, collection, and evaluation. The engineer will synthesize findings into clear, actionable communications and work closely with researchers and engineers to turn qualitative judgment into scalable processes. The work is fully remote and is an independent contractor engagement expected to last three months, with a start date as soon as possible. Compensation is at market rate; applicants are asked to provide a specific rate expectation. The preferred availability is 40 hours per week, with at least six hours of overlap with Pacific Time. The evaluation process is approximately 30 minutes and consists of one technical and cultural discussion.

Requirements

Proficiency in Python, particularly building and maintaining pipelines and infrastructure, or in Typescript.
Hands-on experience with preference data collection and/or rubric creation.
Strong written and verbal communication skills, including the ability to distill technical work into clear, actionable summaries.
High-taste judgment and the ability to articulate why something is good, not merely that it is.
Availability for at least six hours of overlap with Pacific Time; 40 hours per week is preferred.

Nice To Haves

Experience contributing to or navigating large codebases such as VSCode.
Availability for 40 hours per week.

Responsibilities

Design, refine, and iterate on rubrics and evaluation criteria for preference data.
Review and label data to a high quality bar, identifying subtle issues.
Build and maintain pipelines and infrastructure supporting data generation, collection, and evaluation workflows.
Synthesize findings from data work into clear write-ups, updates, or recommendations.
Collaborate with researchers and engineers to translate qualitative judgment into scalable processes.